Automobile sensors may usher in self-driving cars

Google last year demonstrated the results
of its research-and-development efforts
to create an autonomous vehicle. The
small fleet of specially equipped cars—six Toyota Priuses and one Audi TT—has logged more than 140,000 miles
of daytime and nighttime driving in
California, including traversing San
Francisco’s famously crooked Lombard
Street and the Los Angeles freeways (Figure 1). In all cases,
an engineer was in the driver’s seat, monitoring each car’s
performance and ready to take over if necessary.

A robocar of the future would be
so intelligent that its driver would be
able to read, play, or work rather than
piloting the car. The benefits would
include safety, freeing up the driver
for other tasks or recreation, and the
more effective use of the traffic infrastructure
due to more efficient traffic
regulation and fuel efficiency.

Motor-vehicle accidents are the
leading cause of death of 13- to
29-year-olds in the United States.
According to Sebastian Thrun, an
engineer at Google and the director
of the Stanford Artificial
Intelligence Laboratory, which created
the Google robocar, almost all
of these accidents are the result of
human error rather than machine
error, and he believes that machines
can prevent some of these accidents.

“We could change the capacity of
highways by a factor of two or three
if we didn’t rely on human precision
for staying in the lane and [instead]
depended on robotic precision,” says
Thrun. “[We could] thereby drive a
little bit closer together in a little bit
narrower lanes and do away with all
traffic jams on highways.”

Doubling highway capacity by a factor
of three with no added infrastructure
costs and freeing an hour or two
a day for productive or relaxing pursuits
seem like worthy goals, but how
close is the auto industry to achieving
a practical self-driving car? Google is
not in the car-production business and
has no business plan for monetizing its
research (Reference 1). In Google’s
approach, autonomous vehicles will
not require a government mandate to
become reality. The Google fleet uses
LIDAR (light-detection-and-ranging)
technology, such as that in a system available from Velodyne’s HDL (high-definition
LIDAR)-64D laser-sensor
system, which uses 64 spinning lasers
and then gathers 1.3 million points/sec
to create a virtual model of its surroundings.
One reason to use LIDAR rather
than radar is that the laser’s high-erenergy,
shorter-wavelength laser light
better reflects nonmetallic surfaces,
such as humans and wooden power
poles. Google combines the LIDAR
system with vision cameras and algorithmic
vision-processing systems to
construct and react to a 3-D view of
the world through which it is driving
(Reference 2).

The enabling sensor hardware in the
vehicles enables the cars to see everything
around them and make decisions
about every aspect of driving, according
to Thrun. Although we are not close yet
to a fully autonomous vehicle, the technology,
including the sensor platform of
radar, ultrasonic sensors, and cameras, is
available in today’s intelligent vehicle.
It remains only to standardize the car’s
hardware platform and develop the software.
Cars are approaching the point
that smartphone platforms had reached
just before the introduction of the Apple
iPhone and the Motorola Android.

As sensors decrease in price and
increase in integration, they will
become ubiquitous in all cars. Once
users accept them as normal parts of a
car, then automotive-OEM companies
can integrate more intelligence into
them until they achieve the goal of an
autonomous car. Today’s intelligent
automobile can perform many driver-assistance
tasks, such as avoiding and
preventing accidents and reducing the
severity of accidents. To perform these
tasks, the vehicles have passive safety
systems, such as air bags and seat belts;
active safety systems, such as electronic
stability control, adaptive suspension,
and yaw and roll control; and driver-assistance
systems, including adaptive
cruise control, blind-spot detection,
lane-departure warning, drowsy-driver
alert, and parking assistance. These systems
require many of the same sensors
that the autonomous car requires: ultrasonic
sensors, radar, LIDAR systems,
and vision-imaging cameras.

Cars now use ultrasonic sensors to provide
proximity detection for low-speed
events, such as parallel parking and low-speed
collision avoidance. Ultrasonic
detection works only at low speeds
because it senses acoustic waves; when
the car is moving faster than a person can
walk, the ultrasonic sensor is blind.

Although ultrasonic-sensor technology
is more mature and less expensive
than radar, car designers who care about
the aesthetics of the car’s appearance are
reluctant to have too many sensor apertures
visible on the car’s exterior. As a
more powerful and more flexible technology,
radar should begin to replace ultrasonic sensors in future designs (Figure 2).

Radar works in any type of weather
and has short-, medium-, and long-range
characteristics. For example,
adaptive cruise control works in the
long range, looking 200m in front of
the car, tracking the car, and accelerating
or braking the car to maintain a
certain distance. Radar also provides
blind-spot detection and lane-departure
warning. Early versions of these systems
audibly warned the driver of an impending
problem, but some implementations
now take control of the car to avoid the
problem. For example, the 2011 Infiniti
M56 has an optional blind-spot-warning/intervention system that relies on
radar scans from the left and the right
rear quadrant of a car. If the radar system
detects a car in the driver’s blind spot,
a light comes on. If the driver activates
the turn signal, an audible beep comes
on. If the driver persists and starts to
move into another lane, the car gently
applies brakes on the opposite side of
the car, moving the car back into the
center of the lane (Reference 3).

Most automotive radar systems currently
are not highly integrated, taking
up significant space, and are costly.
Analog Devices’ recently introduced
AD8283 integrated automotive-radar-receiver
analog front end represents the
increasing integration that decreases
the size and cost of automotive radar
(Figure 3). It will sell for about 50% less
than a discrete design for an automotive
analog front end and fits into a 10×10-mm package. “The market is moving
toward putting radar into nonluxury
vehicles—cars for the rest of us,” says
Sam Weinstein, product manager for
the Precision Linear Group at Analog
Devices. The sample price for a six-channel
AD8283 is $12.44 (1000).

IR (infrared) LEDs and photosensors
find use in automotive applications,
such as rain sensing/wiper activation on the BMW 7 series and the Ford Edge.
Sophisticated IR cameras enable safety
applications, such as drowsy-driver
sensing, which is also an option in the
Mercedes E550 sedan. Drowsy-driver
sensing uses an IR camera to watch the
driver’s eyelids to tell whether they are
blinking rapidly, indicating that the
driver is alert, or blinking slowly or
even closing. The car emits an audible
warning or vibrates the driver’s seat.

Out-of-position sensing similarly
uses IR cameras. Today’s passenger seats
must have pressure sensors to determine
the weight of the passenger and use the information to deploy the passenger’s
air bags. The air bags deploy at different
speeds, depending on the weight
of the passenger. This sensor does not
know, however, whether the passenger
is leaning on the dashboard, reclining
in the seat, or moving to the left or
the right. The closer the passenger is
to the deploying air bag, the greater
the impact. The camera monitors the
passenger’s position, and, upon impact,
deploys the air bag appropriately to the
passenger’s size and position.

These cameras use IR LEDs rather
than those in the visible spectrum
because they must be able to work at
night. It would be distracting to illuminate
the driver or the passenger with
visible light for the camera to sense.
The human eye detects light as visible
at distances as great as approximately
700 nm, whereas IR cameras detect 850-
to 900-nm-distant light.

IR imaging also has a place outside
the car for crash avoidance, and these
applications require IR illumination.
According to Sevugan Nagappan, marketing
manager of the infrared business unit at Osram Opto Semiconductors,
IR cameras can help in collision avoidance
by seeing beyond what the high
beams illuminate. “IR-LED illumination
allows you to see when you can’t
have your high beams on to see past
your headlamps, for example, allowing
the system to see beyond the headlights
to see and avoid a deer entering the
road,” he says.

IR LEDs’ primary use has so far been
in remote controls. However, these
inexpensive LEDs use 10 mW or less of
power. Automotive applications require
output power of greater than 1W to
illuminate the subject. In addition, the
IR LED must be small enough to fit next
to the IR camera and be inconspicuous.
Nagappan estimates that the camera
needs to measure less than 10 mm2,
and illuminating the IR LED requires
5 mm2. He says that manufacturers can
make LEDs in small packages that can
provide 3.5W and that these devices
are enabling new applications. Osram’s
3.5W SFH 4236 IR LED has an integral
lens with a narrow beam angle to focus
the IR light, increase the beam intensity,
and focus the beam into the eye box
to watch the driver’s eyes.

Innovation is also driving down the
cost of the cameras. The Fraunhofer
Institute expects to bring to market a
camera as small as a grain of salt and
costing only a few euros. The resolution
currently is 250×250 pixels. These
cameras could replace side-view mirrors,
reducing airflow drag (Figure 4).

Editor's note: The original version of this article contained an error, which has been corrected in Figure 3 above and in the associated PDF file. "AFE: Analog front end" was changed to "AAF: Anti-aliasing filter" on May 26, 2011.